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The influence of mergers and acquisitions on the innovativeness of pharmaceutical companies in the global economic up- and downturns of 1997 – 2008

Author: Ivo Tokarski, 2012.
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This research gives insights on the issue to what extent mergers and acquisitions (M&As) of global pharmaceutical companies influence their innovativeness in economic up- and downturns. In total 1615 pharmaceutical companies were investigated through an empirical analysis. Evidence for three main findings has been found. First, pharmaceutical companies that acquire in slight economic downturns have higher positive returns. Secondly, the number of patents that have been traded does not influence the returns of the company – therefore the innovative performance does not increase measurably. Thirdly, the location of US based target firms has a positive effect on the number of patents that have been traded.

Keywords: Mergers, Acquisitions, Pharmaceutical Companies, Innovativeness, Economic upturn, Economic downturn.

1.    Introduction

In the pharmaceutical industry companies are reliant upon scale and innovativeness to develop products and stay profitable by covering their expenses for R&D. To stay competitive and profitable the companies require therefore large cash reserves. This needed affluence in cash reserves is restrained when a recessionary period strikes the global economy. By investigating how those companies adapt their M&A behaviour to these new economical circumstances and measuring what is affecting their innovativeness, will provide managerial insights on which strategy could pay-off in the end.

Managers and CEO’s of pharmaceutical companies are dealing with a competitive surrounding in which they have to guide their enterprises through the increasing rivalry within their industry sector caused by new technologies, pioneering research and innovation to secure the next blockbuster drug. An additional major concern is the threat of appearing substitutes, which could replace the patented pharmacological products and therefore cut the companies existing commercialization chain – leaving them behind on huge R&D costs, which cannot be compensated for anymore. Furthermore, the economic cycles create another challenge in raising funds and marketing the products to keep the innovativeness up. Those companies and their leaders have to find new ways to secure their innovativeness on the one hand, and block out their competitors on the other. An established method that pharmaceutical firms seem to rely on is mergers and acquisitions (M&As), in order to acquire valuable patents to increase their potential for innovativeness, as well as a method to block out competition strategically.

Previous researchers like Pisano (1997) found out that based on transaction cost theory, M&As compared to alliances, have lower transaction costs in cases that involve insecure property rights and, transaction-specific production goods and when a transfer of complex technology is needed. Basing on the study of Pisano (1997) the pharmaceutical industry follows a logical pattern using M&As as a form of collaboration that have a low transition cost.

Contrasting the idea of transaction cost theory of the pharmaceutical industry towards industry non-specific studies of Kohers and Kohers (2000), shows that M&As in general create slightly negative shareholder value, which gives an indication that even when transaction costs are low – no value is created. Supporting this statement is the study of Moeller et al. (2005), who studied the merger wave of 1998 – 20001 and found out that more value was destroyed than created within this period.

This research will conduct therefore an empirical analysis of the global pharmaceutical industry, in order to investigate how the innovativeness of these acquiring companies is affected by mergers and acquisitions – in the context of economic up- and downturns.

The scope of a global perspective shall have the purpose to distinguish if the number of patents as an innovativeness indicator has an influence on various other performance indicators, which could give important managerial insights.

The focus is laid on global pharmaceutical firms that possess SEDOL codes as an identification standard, so that necessary data can be obtained from several data sources (e.g. “DataStream” and “SDC”) – since those companies are mainly stock listed and obliged to provide financial data to stockholders. Another aspect of focusing on global pharmaceutical firms is related to previous researches that have shown that firm size effects innovation (Bouncken, 2011; Giovannetti et al., 2011), which in this perspective can be translated into the theoretical finding that by investigating large sized companies there should be a measurable effect. Complementing the view from another perspective the research by Christensen (2006) gives indications that small sized companies lose their innovativeness when being acquired by large ones because of the organizational structure. This frames the focus on global companies by adding the negative implication for small sized companies, which gives theoretical insights that when a large sized global firm acquires a smaller company the influence on innovativeness is measureable (Bouncken, 2011; Giovannetti et al., 2011) and should have a negative effect on the small company itself (Christensen, 2006). But what effect does it have on the acquiring company?

To set a researchable timeframe I chose the fifth and sixth global merger waves, which took place in between 1997 – 2008 (see figure 1). This gives the chance to distinguish two economic upturns and two economic downturns, creating a basis for comparison and validity.

This classification creates an opportunity to establish how the M&As of pharmaceutical companies affected the innovativeness of the acquiring companies within this closed time period that had economic upturns as well as economic downturns.

2.    Theoretical reflection

To successfully conduct this research, two main factors have to be put into theoretical perspective when defining them, namely ‘Patents’ and ‘Periods’ – as this research needs to establish measurement criteria for innovativeness (indicated through patents), and economic up- and downturns given by two upturn and two downturns periods.

A big part of the scientific literature believes that measuring innovativeness in any industry is difficult per se, because of the fuzzy logic behind it. When it comes to measurements like R&D spending’s and similar innovativeness indicators it is rather suitable to compare a small amount of companies that have no diverse demographics. When taking a global approach to examine whether there is a general pattern for the worldwide pharmaceutical industry, innovativeness has to be defined by a common measure that is internationally independent. Therefore, the number of patents that have been traded to the acquiring firm by M&A, shall distinguish the transition of innovativeness (potential) from the target company, to the acquirer. Further, this research is focused on examining whether this transition of patents from the target company to the acquirer, has performance benefits for the acquiring company in economic up- or downturns.

The timeframe is set within 1997 – 2008 because it gives additional insights of the performance in two economic upturn periods and two economic downturn periods.

The balancing act of the acquirer in this setting is described as a need to innovate in order to stay in business, which on one hand should focus on not restricting the innovation process, but on the other hand should guide to preferred destinations (Pal, 2010). This means that there should be incentives to foster innovativeness but in a preferable direction for later commercialization. If it is not possible however, the here researched M&As give the opportunity to acquire innovativeness through patents, which fit into the company’s product pipeline.

2.1 M&A influence

Mergers and acquisitions are the main factor of this research and need to be defined in order to extract a measureable dependent variable for the statistical calculation. To evaluate which measure will be the most suitable I will guide myself by already established theoretical findings of scientists that have published articles under this aspect.

The authors Cassiman et al. (2005) performed an empirical analysis of the role of technological- and market-relatedness of M&A on the R&D process, and found out that technological and market- relatedness between M&A partners distinctly affects (amongst other factors) the performance and organisational output. This finding gives an implication that when calculating on quantitative data, a distinctive measure for M&A influence should be the performance of a given company.

The motives for M&As can be classified overall into adaptive or defensive rationales, versus proactive or offensive rationales (Burns et al., 2005). For the industry context of pharmaceutical companies those classifications do also apply because some M&As were performed in order to fill gaps in a company’s product pipeline to maintain growth (Grabowski et al., 2002), so were rather defensive in nature, whereas other M&As were focused on the proactive rationale of increasing the scale, scope and R&D productivity (Cockburn & Henderson, 2001).

Further implications for M&As in the pharmaceutical industry are related to a study of the authors Danzon et al. (2007), who found out that firms with a relatively old portfolio of marketed drugs exhibit a higher propensity to acquire another firm. It can be argued that economic stress is influencing the M&A activity, which should well relate to the economic up- and downturns.

In order to measure the influence of M&As this study will adopt the idea of the authors Arza and Lopez (2011), who showed that companies linked to research organisations invest more in innovative activities and are more prone to patenting – which gives a theoretical hint of the linkage between the number of links and innovativeness.  Therefore, this research shall define the ‘M&A influence’ as the amount of linkages and how often they have been created between the acquiring company and any given target, in other words – the frequency of M&As that have been performed in a given period. This shall be one of the dependent variables of the statistical analysis that should answer what influence the frequency of M&As have on the company’s innovativeness.

Interestingly, there are different motives and frequencies of M&As when comparing global pharmaceutical firms. The research of Demirel and Mazzucato (2010), has found evidence that the location of the most innovative US based pharmaceutical firms is associated with their location. Taking this into account and extending it to a global perspective the question arises whether the different locations of the targets that have been acquired, effects the innovativeness of the acquiring company differently. Also the question of whether demographic areas had different strategies to cope with the fluctuations of the global pharmaceutical market and their implications can be additional insights of this study and broadening the focus of patents and periods by adding another variable.

Therefore following hypotheses shall be tested:

H1: The location of the target firm has an effect on the number of patents that have been traded.

2.2 Innovativeness

The second central factor in the theoretical construct of this study is establishing the innovativeness of a given pharmaceutical company. There are different methodologies to do that, for example the ‘soft’ approach of the authors Marvelakis et al. (2006) who presented a methodology based on measuring and benchmarking innovativeness with fuzzy logic through an innovation survey. They included interrelated measurements like the company’s innovation process, the products developed, the innovation process utilized and the way in which the innovation process is project managed. The same problem as in the performance measurement arises when trying to place those measurement indicators on this research. Because of the global diversity of those ‘soft’ factors, this study will focus rather on ‘hard’ data, which is more exact to compare. The authors Mairesse and Mohnen (2002) presented in their study an illustrative framework for accounting for innovation and measuring innovativeness, which can be regarded as measuring ‘hard’ data in context of innovativeness. Their approach of an accounting framework based on the measure of innovative sales, which gives insights of the validity of this measurement factor in order to distinguish the innovativeness of a firm.

Further, Lanjouw and Schankerman (2002) have researched the patent quality and research productivity by measuring innovation with multiple indicators. One of them was the quality of patents that allowed making a judgement on how productive a company was in terms of R&D. For the research of pharmaceutical companies this measurement criteria should be changed into the amount of applied patents for each given company. As for the other central factors, this innovativeness indicator gives grounds to compare pharmaceutical companies in a global context, without having to constrict the results due to the complexity of the different geographical environments those companies encounter.  Interpreting the research of Lanjouw and Schankerman further, this patenting frequency should be positively related to the stock market value of firms. The stock market value is on the other hand related to the financial performance of the firm, which connects the innovativeness to the above mentioned performance factor and should also in some way relate to the M&As of the firm.

The pharmaceutical industry has its own implications regarding the measurement of innovativeness, because it heavily relies on patenting – were the patent virtually equals the product (Thomas, 2003). Within this industry the patented products could be easily and cheaply reproduced by competitors, when this protection barrier would not exist. And since the capital investment in creating a marketable drug is exceptionally high and has to pass several R&D stages and FDA approval, patent protection is crucial to innovative companies in order to retain a guaranteed period of market exclusivity, which should give the opportunity to recoup the development costs (Bale & Harvey, 1997). For that reason my research will adopt the number of applied patents that have been traded from the target company to the acquirer as a variable to measure the potential increase of innovative output of the acquiring company.

Following hypothesis shall therefore also be tested:

H2: The number of patents that have been transferred from the target to acquire have a positive effect on innovative performance.

2.2.1 Innovation and the moderator: Structural inertia

As the pharmaceutical companies compete in an increasingly technologically and scientifically sophisticated market, their capabilities in researching and developing technologies for product development are crucial for their success (Takayama et al., 2002). The possible implication deriving from this can be defined as structural inertia (Hannan and Freeman, 1984), because the average pharmaceutical company prefers to refine and market the same product, which ensures stability for the firm (Takayama et al., 2002).  In the case of high R&D intensive industries like pharmaceuticals, the result of too much strengthening of a specific core field may stifle innovation and increase the inability to move quickly into complementary or different product areas (Takayama et al., 2002). Interesting to see is however, that according to a study by Leonie Tichelaar (2009) on the relationship between organizational size and age, structural inertia and product innovation in the Dutch pharmaceutical industry – the inertial forces are not stronger in large and old Dutch pharmaceutical firms than in small young ones. This finding gives a clue about the paradox the pharmaceutical firms are experiencing. In order to escape structural inertia and implement ‘really-new’ or ‘radical’ innovations (Garcia and Calantone, 2002), the firms seek outside their boundaries to acquire preferably smaller and cheaper companies in order to stimulate innovation. But the general disadvantage of M&As turns out to lay in the organizational inertia in any established firm, which is being acquired, that raises the cost of integrating the acquired firm into the own system and brining its technologies to a common level (Lall, 2002) – therefore implying that not only old large firms but also young and small ones have common grounds for structural inertia. This seems to be the major opposition force towards the transaction cost theory (Pisano, 1997), which seems to lose validity as structural inertia increases.

In global downturns many businesses experience an increased uncertainty within their industry due to the turbulence in the financial market (Sahin et al., 2011). Even though the pharmaceutical industry seems to be a special case, since there is no recessionary period for illness and need of medication, they nevertheless will be affected by the reduced financial flow caused by the recession. The capital-intensive business model of large pharmaceutical firms is based on high R&D expenditures with only a few products to recoup the investment that went into the development and marketing of the products. As the large pharmaceutical companies were ‘lumbering’ ahead in their early growth phases to attain scale economies and high turnovers, they were sufficient in covering their own investments in product developments (even though financial leverage through debt and/or outside investors was appreciated because of risk reduction and tax savings).  As those companies grew in times of prosperities, they were able to put some financial ‘meat’ on their structural bones, allowing them to survive economic downturns and global recessionary periods for a certain time. Smaller biotech/pharma companies caught by a global recession in their early stages of development do not have the means to attract financial investors and loans in this economic climate of uncertainty, which varies strongly over time – with uncertainty levels rising by 50 percent to 100 percent during recession (Bloom, 2007). Companies with little cash reserves will find themselves soon in the position of standing with their back against the wall, with the only chance of survival by being acquired through a large(er) pharmaceutical company that has got the necessary the cash reserves. The problematic situation for the smaller companies is that they will not have any bargaining power towards the acquiring firm, since their economic survival is dependent on the bigger firms. For the large pharmaceutical companies on the other hand it seems like a global economic downturn is like a private invitation of the top performers of the industry to go on a ‘cheap shopping tour’, buying out prospecting companies along with their patents in order to increase their product portfolio. The question for the acquiring companies should be whether they should integrate those M&As or leave them separate by providing only financial support, since the study of McCarthy and Weitzel (2009) has shown that compared to large firms, acquiring SMEs, are more flexible and more able to avoid deals that turn sour. This new strategic implications could show that bridging the financial needs of a start-up in order to let it grow by itself, could be more profitable in the long run, than vertical integration.

Following hypothesis shall therefore be tested:

H3: Pharmaceutical companies that acquire in economic downturns have higher positive returns.

3.    Methodology

3.1 Sample

The initial raw data sample was extracted through the database SDC, which included all mergers and acquisitions that have been published. The date of announcement for the mergers and acquisitions was narrowed down to the timespan of 01/01/1997 to 31/12/2008. Acquiring companies were narrowed down to companies that possess the primary SIC Code of 2822, 2834, 2835 and 2836. Further, the deal type code (1) was included, as well as the deal status code (C) and the acquirer public status code (P). In total n=1615 deals could be extracted.

The raw data set was then completed by adding financial data extracted from Datastream by searching companies through the SEDOL code that has been coded to a mnemonic expression. The data for the financial performance indicators covers 1137 of the 1615 M&A deals.

Additionally, the data on patents has been counted manually by researching the European Patent Office database (EPO), and counting the patents that have been filed until the day of the M&A. In total patent data for 970 companies could be extracted.

The performance indicators for this paper will be set into the context of financial indicators that will allow to make a ‘hard’ data based analysis.

This research shall base on one of the most extensively studied areas, namely the financial component of how economic goals of the company are fulfilled (Barney, 2002). It is rather not beneficial to use the extended model of performance measurement because it gives to complex results for the regression analysis in order to analyse the pharmaceutical companies in global terms.

The financial perspective is defined by the tangible outcomes of the strategy using traditional financial terms, like economic value added, revenue growth, costs, profit margins, cash flow, net operating income, etc (Grigoroudis et al., 2012). In respect to that in this study I will also define further dependent variables influencing innovativeness in terms of cumulative abnormal returns of a given company. There are several reasons for that. Firstly, the availability of data for different measurement indicators like e.g. ‘economic value added’ or ‘profit margins’ are hard to obtain, and mostly not publically available. The return index of pharmaceutical companies on the other hand is available because most of the companies are listed on the stock exchange and therefore need to publish these numbers. Secondly, the cumulative abnormal returns indicate the ability to capture value from the commercialization of products, which gives a variable that includes several indicators of whether the product or innovation is commercially successful from the beginning of the R&D to marketing. Third and lastly, it also gives common grounds to compare companies on a global level because regional taxes are excluded and the focus lays just on the commercial success of income generation. Therefore, cumulative abnormal returns shall be the other main variables in defining the performance measurement statistics, which goes along with the authors Dehning and Richardson (2002) who showed in their research synthesis, on how to calculate returns on investment in a complicated industry like IT, that a wide range of performance measurements can be used, including event studies based on the shareholder return, on stock performance and sales growth percentage ratios.

The research setting for this study is defined as the global pharmaceutical industry. In total 1615 companies were included which acquired other companies within the timeframe of 1997 – 2008. This timeframe was divided into four periods that are defined as economic upturns (Period 1, Period 3) and economic downturns (Period 2, Period 3). The division of into those four periods corresponds with the fifth and sixth global merger wave of pharmaceutical companies (, 2012).

Figure 1 – Merger waves in pharmaceutical industry 1997 – 2008

3.2 Dependent variables

The following section presents the dependent variables that have been chosen to distinguish the behaviour of M&As in terms of frequency, in terms of the change in percentage of returns to the shareholders in long-term, the acquiring companies stock price value change in long-term, as well as a short-term measurement of the cumulative abnormal return. These dependent variables have the purpose of indicating if there are performance indications in long-term, short-term or in relation to the activity in M&A pursuance of the acquiring company.

Frequency of M&As (freq_ma)

The amount of mergers and acquisitions that have been performed within the specific period, in other words the frequency of M&As, is set to be the dependent variable. According to the authors Cassiman et al. (2005) M&As affect performance and organizational output. Setting this dependent variable into contrast to the independent and control variables, should give insights about how the activity of acquirers influence the other factors. It should give valuable strategic insights of whether an active or rather passive M&A behaviour has better implications on later performance and innovativeness. The data could be obtained by counting every M&A the acquirer performed in a given period, therefore covering n=1614 companies. This variable is additionally a good indicator of how risk averse an acquirer is and if it has do with economic up- and downturns, or locations when controlling for them.

Total Return Index – Abnormal return growth (tri_ar)

The abnormal return growth rates of the total return index are calculated on the basis of CAR (-3, -1) Quartiles, as the estimation window, which has been set into contrast to the CAR (0, +1) Quartiles as the event window. This adjusted growth rate has been benchmarked to the growth rates of the total return index global pharmaceutical industry. The t-test shows significance in comparison of the population means with an indicator of 0,26.

The abnormal return growth rates of the total return index, is a measurement to show to which extent shareholder value has been created. This measurement shows if the M&A in the end has created shareholder value in the long-term.

All data has been extracted from Datastream and accounts for n=1137 of the acquiring companies.

Figure 2 – Abnormal return growth rates, Total Return Index

Price Index – Abnormal return growth (pi_ar)

The abnormal return growth rates of the price index of the acquiring company has been calculated as the above-mentioned variable (tri_ar) with CAR (-3, -1) Quartiles as the estimation window and CAR (0, 1) Quartiles as the event window. The adjusted abnormal return has been benchmarked to the price index of the global pharmaceutical industry. The t-test shows also significance at an indicator of 0,47.

This variable is a more pessimistic version of the (tri_ar) variable, and displays to which extent the stock price of the company has been affected through the M&A, in respect to growth percentages. This measurement shows whether the company itself has profited from the M&A in the long-term. The coverage is also n=1137 of the in total n=1615 acquiring companies

Figure 3 – Abnormal return growth rates, Price Index


Cumulative Abnormal Return (car21_1)

An additional dependent variable is calculated by extracting the cumulative abnormal return on a daily basis of -21 days before the M&A, as the estimation window, benchmarking the average to the industry standard, and in the end contrasting this performance to +1 day after the M&A. This variable has the reason to distinguish the companies’ performance on daily basis by examining the return index and not on a quarterly basis like the variables (tri_ar) and (pi_ar). Further, this variable does not investigate the growth rates but the financial performance of the given company and therefore is a suitable measure to distinguish the firms’ returns in the short-term. The coverage of this variable is n=1136 of in total n=1615 acquiring companies.

3.3 Independent variables

Number of patents (num_pat)

The main independent variable for this research is the number of patents that has been traded as the acquiring firm merged or acquired the target company. The number of patents has been extracted by checking each individual target company in the European Patent Office database, and counting the patents they have possessed until the target company has been acquired. The patents that have been traded by M&A from the target company to the acquirer are set to be an indicator of the potential for innovativeness that has been transferred to the acquiring company.

3.4 Control Variables

In general, this study is controlling for periods, location and what influence the periods with locations have on the innovativeness given by the number of patents.

Locations (row_tar), (us_tar), (eu_tar), (ni)

Each Location has been coded with a dummy variable in order to distinguish the areas in which the target companies have been acquired. The variable (us_tar) are US based targets, (eu_tar) distinguish the European based target and all other countries are covered through the variable (row_tar), which stands for the rest of the world. Additionally the variable (ni) is giving insight whether the deals are national or international which provides information about the acquirer.

Periods (p1), (p2), (p3), (p4), (u), (d), (tp)

When controlling for periods the deals have been coded with a dummy variable for each period in that the acquisition took place. Ranging from p1 to p4 the individual periods cover the timeframe of all mergers and acquisition within 1997 – 2008.

(p1) is defined as the period from 1997-1999, (p2) is 2000-2002, (p3) is 2003-2006 and (p4) is 2007-2008. Additionally periods are coded as economic up- and downturns, in which (p1) and (p3) display upturns given by the variable (u), and the periods (p2) and (p4) are described by the variable (d).

Further, turning points (tp) defined as peak performances in the abnormal return growth rates are included into the model by an established event window of -1 Quartile and +1 Quartile from the given max/min of the (tri_ar) and (pi_ar).

3.5 Descriptive statistics

Variable        Obs     Mean              Std. Dev.         Min     Max

freq_ma          1614   2.67658         2.643193       1          18

num_pat         970     128.0495       100.4585       1          309

tri_ar               1137   510.044         297.3644       1          1025

pi_ar                1137   506.197         297.7779       1          1025

car21_1          1136   534.6268       301.4829       1          1067

p1                   1615   1.165944       .3721458       1          2

p2                   1615   1.214861       .4108529       1          2

p3                   1615   1.373375       .4838502       1          2

p4                   1615   1.24582         .4307059       1          2

u                      1615   1.539319       .498606         1          2

d                      1615   1.460681       .498606         1          2

tp                    1615   1.454489       .4980787       1          2

row_tar           1615   1.260062       .438804         1          2

us_tar              1615   1.492879       .5001041       1          2

eu_tar             1615   1.247059       .4314348       1          2

ni                     1615   1.367183       .4821862       1          2

rowp1             1615   1.032817       .1782134       1          2

rowp2             1615   1.040248       .1966004       1          2

rowp3             1615   1.104025       .3053872       1          2

rowp4             1615   1.082972       .2759255       1          2

usp1               1615   1.092879       .2903531       1          2

usp2               1615   1.117647       .3222895       1          2

usp3               1615   1.182663       .3865093       1          2

usp4               1615   1.09969         .2996796       1          2

eup1               1615   1.040248       .1966004       1          2

eup2               1615   1.056966       .2318493       1          2

eup3               1615   1.086687       .2814634       1          2

eup4               1615   1.063158       .2433221       1          2

ltri_ar              1615   1.063777       .2444311       1          2

lpi_ar               1615   1.064396       .2455336       1          2

lcar21_1         1138   1.093146       .2907645       1          2

lfreq_ma         1615   1.091022       .287729         1          2

(For the correlation matrix please see Appendix A.)

3.6 Calculation Model

The basis calculation model of this study is determined by finding the influence of the number patents that have been transferred from the target company to the acquirer by merger or acquisition, on four different performance indicators. The performance indicators are given by the dependent variables, namely, (car21_1), (tri_ar), (pi_ar) and (freq_ma).

The calculation includes three calculation models on which each dependent variable is tested individually for each control variable. The control variables are divided into control variables for periods, as well as control variables for locations. The independent variable is defined as the number of patents.

First an univariate regression analysis is being performed on which the individual performance shall be indicated. The calculation model for the univariate analysis contains:

Dependent = Patents

Dependent = Periods

Dependent = Locations

Dependent = Patents x Periods

Dependent = Patents x Locations

Dependent = Patents x Periods x Locations

The second calculation is a multivariate analysis, in which the four dependent variables, indicating the performance, are controlled by the same independent and control variables as mentioned above (Dependent = Independent + Control).

The third calculation model is a multinominal logitistic regression, which adds the likelihood ratios to the analysis. The performance indicators given by the four dependent variables have been coded displaying positive (coded as 1) or negative (coded as 0) relationships for the dependent variables (car21_1), (tri_ar), (pi_ar). For the dependent variable (freq_ma) the weighted average has been set as a benchmark and if the frequency of mergers and acquisitions for a given company scored above the average, coded as 1, and if it underperformed the average it has been coded as 0. In the dataset those coded variables are called (lcar21_1), (ltri_ar), (lpi_ar) and (lfreq_ma), respectively.

LDependent= Patents

LDependent = Periods

LDependent = Locations

LDependent = Patents x Periods

LDependent = Patents x Locations

LDependent = Patents x Periods x Locations

4. Results

This section contains the summarized result tables of the regression analyses in sequence of univariate regression, multivariate regression and multinominal logistic regression. For each block the main results are presented. For the complete calculation output please see Appendix B, C and D.

4.1 Summary of findings – univariate regression

The univariate regression shows that overall the frequency of M&As is lower in economic downturns than in upturns, with negative implications for financial returns at turning points. Further, it shows that acquiring US based targets in economic downturns has a positive influence on the growth of the stock prize of the acquiring company, as well as for the growth in shareholder value.

Those findings indicate that acquiring in moderate economic downturns, has the best financial implications for the acquirer, especially when they are based in the US. Avoiding turning points, which indicate peak performances in the market, could lower the risk of fluctuations in the market. Further, acquiring companies in moderate economic downturns could mean to get a cheaper deal when performing M&As (see figure 5 and 6).

4.2 Summary of findings – multivariate regression

The multivariate regression shows that when the frequency of mergers and acquisition increases, also the number of patents that have been traded increases. Further it confirms the pattern of the univariate regression that turning points in the market should be avoided, because they account for highly negative performances, especially in generating shareholder value.

Those findings indicate that a high frequency of M&As indeed increases the patent flow to a company, which could increase their potential of generating innovations – but on the other hand no measurable financial performance indicator can be associated to the amount of patents a company acquired. It seems like not the amount but rather the quality or fit of the patents could have a stronger impact on performance.

4.3 Summary of findings – multinominal logistic regression

The multinominal logistic regression distinguishes a similar pattern as the analyses above, by showing that positive returns are significantly associated with economic downturns, whereas negative returns are associated with economic upturns. Interestingly, this calculation shows additionally a pattern that the likelihood of a given location in period 3 (economic upturn) to generate abnormal returns is always negative, whereas the likelihood of generating abnormal returns in period 4 (slight economic downturn) is mostly positive. Further, it can be said that national M&A deals account for the best abnormal returns.

Those findings indicate that (regardless of location) acquiring companies in slight economic downturns, indicated by period 4, has positive financial implications for the acquirer. As mentioned in the results of the multivariate regression, slight economic downturns could have benefits of getting cheaper deals or even benefitting of reduced risk because a slight downturn can be seen as a temporary phase after which the market regenerates for further growth. Further it can be interpreted that national M&A deals account for positive abnormal returns because the transaction costs are the lowest when integrating a company that functions in the same market and under the same legislation.

5. Discussion

This section is structured alongside the calculation models and discusses the implications given by patents, periods and locations. The hypotheses will be answered in this section accordingly.

5.1 Patents

The number of patents that have been traded does not influence the returns of the company – therefore the innovativeness does not increase measurably.

  • No significance on number of patents on performance.

The univariate regression shows no significance as it comes to the number of patents that have been traded from the target company to the acquirer. They rather display that the abnormal returns, being it growth percentages or the cumulative abnormal stock returns, do not increase with the number of patents. It could be the first indicator that patents in the pharmaceutical industry after all, are not related with the companies growth nor the stock performance. When contrasting these findings to the study of Hagedoorn and Duysters (2002), who found out that acquisitions do contribute towards innovativeness of the acquiring firm, when there is an organisational and strategic fit – the question arises if the majority of US based M&As, had no organizational or strategic fit. Further research is needed to distinguish what motives the US acquirers really had, and if they have relied to heavy on the transaction cost theory.

  • The frequency of M&As influences the number of patents that have been traded.

All performed regressions in this study indicate a positive trend as it comes to the frequency of M&As and the number of patents that have been traded. As it comes to this research it is a logical implication that if the frequency of M&As goes up also the number of patents that are transferred increases. Nevertheless, this result underlines the validity of this approach to distinguish further, if there are related performance increases by financial or growth indicators.

The analysis shows no significance as it comes to the correlation of the number of patents and returns. It is possible that this is an indicator of a rather strategic nature of acquiring patents through M&As than for new product developments. The authors Artz et al. (2010) found out in their longitudinal study of the impact of R&D, patents and product innovation on firm performance, in which they investigated 272 companies in 35 industries, that patents had a significantly negative relationship on ROA and sales growth. Their study findings combined with the results of this research begs the question whether patents can really indicate innovative performance as it comes to financial measures – or do firms use patents rather as strategic weapons to secure their own position and block out competitors. Further research would be needed to investigate to what extent traded patents generated product innovations that can be commercialized, or whether those product innovations come from internal R&D.For the above mentioned reasons hypothesis 1, stating that the number of patents that have been transferred from the target to acquirer have a positive effect on innovative performance, cannot be proven and has to be rejected.

5.2 Periods

Pharmaceutical companies that acquire in economic downturns have higher positive returns.

  • Acquiring in downturn periods has better returns than acquiring in upturn periods.

This research proves that performing deals in slight economic downturns given by period 4 is beneficial for the short- and long-term performance of the acquiring company, especially when it comes to national deals. It seems like it is a good strategy to use a countercyclical investment strategy, to acquire and grow market share in economic downturns. The research of Baghai et al. (2008) supports this statement with their conclusion. They have researched different industries in economic up- and downturns and figured out that the most shareholder value and the biggest company growth, was exhibited by companies performing M&As in economic downturns. They also highlight that most companies behave in counterproductive ways, because of the financial implications an economic downturn brings with it by reducing available funds and increasing the uncertainty. My research is in line with their findings and adds another insight, attributed to the turning points.

  • Avoiding turning points by +1/-1 Quartile when acquiring companies is essential to not make negative returns.

The statistics show that peak performances, defined as turning points, should be avoided by the timespan of approximately (+1 Quartile; -1 Quartile), otherwise the returns will have a significant negative result. Therefore, concluding it can be said that not peak performances of the market should be aimed at when performing mergers and acquisitions, but rather slight economic downturns.

The univariate regression analysis gives a good insight on the periods that are defined as economic up- and downturns and the turning points. The cumulative abnormal return (car21_1) shows a positive significance for the economic upturn in period 2 as well as for the turning points, showing that the short term returns are following the economic curve of the market (see figure 4 for orientation).

Figure 4 – Performance of Global Pharmaceutical Index 1997 -2008

The positive significance is supported by the results of the variable concerning the abnormal growth percentage for the price index (pi_ar) for the global pharmaceutical industry. Interestingly, the variable describing the abnormal return of growth percentage of the total return index (tri_ar), shows a countercyclical trend for the turning points (see Result section). The variable (tri_ar) therefore gives insights of the risk for the shareholders, that even though the price index indicates a positive correlation for the acquiring company itself, it fails to generate shareholder value in the long run.

The turning points in the analyses show significantly bad abnormal returns when it comes to the creation of shareholder value and also in not increasing the stock price value of the acquiring company. It seems like acquiring other companies in the timespan of (-1 Quartile; +1 Quartile) of the max / min performance of the global pharmaceutical index, is negatively correlated to value creation for the company itself.

An explanation for this finding can be that the value of the deals that have been performed in those periods were more expensive than in the other periods and drained therefore the funds away from the company, lowering the shareholder’s equity.

When looking at figure 5, a pattern of high valued deals emerges, especially in period 2 and 3 and their turning points.

Figure 5 – Deal values of pharmaceutical firms 1997 – 2008

If we take a step further and take the inflation adjusted deal values and benchmark them to the industry average the pattern intensifies (figure 6).  It turns out that in the economic upturn periods the deal values far exceeded the industry average and the turning points indicate the highest peaks of spending habits.

Figure 6 – Inflation adjusted deal values benchmarked to industry average 1997 – 2008

Going further, the statistical analysis proves also that companies that acquire in economic downturns have a significantly higher likelihood of turning out positive returns, than companies that acquire in economic upturns. Therefore hypothesis 2, stating that pharmaceutical companies that acquire in economic downturns have higher positive returns, is proven.

5.3 Locations

The location of the target firms has an effect on the number of patents that have been traded.

Throughout this research especially US based targets struck attention. The results indicate that most of the target firms were US based and have been acquired by national deals. Interesting however, is the bad financial performance these deals had as a consequence.

  • US based companies seem to have other motives to perform M&As than just to increase their patent portfolio.

The US companies show a high activity as it comes to mergers, especially in period 2 defined as an economic downturn. Through their increased activity a significant number of patents has also been transferred, which theoretically should have resulted in an increase of the likelihood to generate innovations. The statistics show however, that quite the contrary took place. While the US was keen to merge and acquire, the rest of the world was rather passive, showed by a negative significance in period 2 as it comes to the frequency of M&As. Accordingly, less patents were transferred and better financial abnormal returns gained by the company and shareholders, in the long- and short-term.

These results indicate that a high frequency of mergers and the increased transfer of patents do not translate into added value, but rather into added costs. It seems like the transaction cost theory of Pisano (1997) is contra-productive in the end. Because as mentioned before, even though costs of integration and the transaction of highly complex technologies can be saved – the costs of the acquisition cannot be regained.

As the US have been always the capitalist forerunner and model of how to expand and grow business by M&A, they should be regarded with more caution by other nations. The hypothesis 3, stating that the location of the target firms has an effect on the number of patents that have been traded – can be therefore be accepted. The US based targets that have been mostly acquired by US acquirers, have also transferred the most patents in national deals.

It begs the question to what extent those deals were strategically motivated in afford to block the competitors out, or to what extent those gained patents have been used to develop new products. It would be up to follow-on research to investigate what the real motives behind those M&As were, and if it is not an out dated misconception of expanding the ‘the empire’. It could turn out that if money that has been spent on M&A would have been redirected towards other business units like R&D, marketing, etc. could have brought higher financial returns. Additionally, it could turn out that the reasons of not redirecting funds towards internal processes may be for the simple reasons that managers just spent so much time on negotiating and completing the deal that in the end they just wanted to have it done no matter what (Prabhu et al., 2005).

5.4 Limitations

At this point there should be also highlighted that this research contains some limitations to it. When taking a global perspective as this research intended the common ground of comparison should be variables that are comparable internationally. The main independent variable being the number of patents that have been filed by the target company until the date of the M&A, have been extracted manually from the EPO database typing in each name (and combination). Unfortunately companies are not listed by the SEDOL or SIC codes, which the financial databases provide, therefore creating the possibility of not covering companies that have changed their name or which are listed under a different name.

Further, as it comes to patents as the measure of innovativeness, there are different opinions to what extent this measure is legit. While the author Griliches (1980) for example states that patents are valid measure to distinguish innovative output, newer research of Hagedoorn and Cloodt (2003) showed that when it comes to complex highly technological industries, any indicator will be good as the other. And since this research is focused to a big extent on innovations, it is still one of the best measures for a quantitative research that includes a global perspective.

Another limitation is the financial performance data that has been calculated on raw data extracted through Datastream. Only data could be extracted from companies that possess SEDOL codes, which are mostly listed on approximately 16 Indices of the world. This limitation has as a consequence that for some locations e.g. the US, there is more data to obtain than for other regions, which creates a possibility of bias.


This research gives insights about the global pharmaceutical industry and how the innovativeness of the companies seems not to be correlated to the financial performance indicators. However, interesting findings could be gained establishing that turning points in the market should be avoided by approximately +1/-1 Quartiles in order to minimize the risk of turning out a deal accounting for negative abnormal returns. More so, this research could prove that M&As performed in overall economic downturns turn out better than those performed in economic upturns. Further it could be distinguished that the best abnormal returns are associated with period 4 that is determined by being as a slight economic downturn. As it comes to locations there can also be valuable information extracted when looking at the US based M&As, which indicate that the US might have other motives then Europe or the rest of the world.

There could be a unique explanation towards why those US companies perform mergers and acquisitions so frequently and trade more patents then others. More than a potential to generate innovations, those patents can be used to block out costly jurisdictional lawsuits against the pharmaceutical firms, which in the case of the US could have dangerous complications since the penalties a given company has to pay are measured on the basis of the total revenue. The research of Bessen and Meurer (2008) investigated this topic thoroughly and concluded that especially in the pharmaceutical industry it makes sense to spend money on patents because the profits associated with those patents are far higher than the aggregate US litigation costs to alleged infringements.

It can be therefore suggested that US based companies in particular are performing frequently M&A deals not only to secure patents on new drugs by saving transaction costs but especially because they want to prevent lawsuits against them. This explanation is in line with the other findings of this study, which show that the EU and rest of the world have an overall lower frequency.


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